As wind turbines are of increasing importance as an environmentally safe and relatively inexpensive energy source, the increased demand for improved wind turbine performance has led to efforts concerning an optimum adjustment of the rotor blades of the wind turbine with respect to the incoming air flow. Typically a wind turbine includes a rotor having multiple rotor blades and a hub. The rotor blades may have a considerable size such that the diameter of a large rotor amounts to 30 meters or more. The rotor blades convert wind energy, i.e. an incoming air flow into a rotational torque that is used to drive one or more generators which are rotationally coupled to the rotor through a drive train.
The boundary layer of the air flow at the rotor blade surface and the distribution of the air flow around the surface of the rotor blade is a major issue when the improvement of the energy conversion efficiency of the wind turbine on the whole is concerned. Many attempts have been made to improve the energy conversion efficiency by using models of the air flow around the rotor blades of a wind turbine. These models are derived from simulations and wind tunnel tests under well-defined operating conditions. In the field, however, the rotor blades of wind turbines experience influences from the rotor itself as well as from three-dimensional flow field conditions with turbulence, from side slip, from roughness and degradation changes of the rotor blade surface which are not addressed in the theoretical models. It has been found that the field data are in many cases completely different from predictions with respect to the aerodynamic and acoustic behavior of the turbine blades.
Among the parameters that affect performance and subsequent deviations from model predictions are, most importantly, (i) unexpected inflow conditions with turbulence and side slip, (ii) the accumulation of debris such as insects, dirt, pollen, etc. on the wind turbine rotor blades, and (iii) differences in performance of individual airfoils and rotor blades, respectively. These deviations may lead to considerable differences between a behavior evaluated from the model and the behavior in the field.